论文标题
真正有用的合成数据 - 评估差异私有合成数据质量的框架
Really Useful Synthetic Data -- A Framework to Evaluate the Quality of Differentially Private Synthetic Data
论文作者
论文摘要
生成合成数据的最新进展，该数据允许添加有原则的保护隐私方法（例如差异隐私）是以保留隐私方式共享统计信息的关键步骤。但是，尽管重点一直放在隐私保证上，但所得的私人合成数据仅在仍然从原始数据中传递统计信息的情况下才有用。为了进一步优化数据隐私和数据质量之间的固有权衡，有必要对后者进行仔细考虑。数据分析师想要什么？认识到数据质量是一个主观的概念，我们开发了一个框架，从应用研究人员的角度评估差异化综合数据的质量。数据质量可以沿两个维度进行测量。首先，可以根据培训数据或基础人群评估合成数据的质量。其次，合成数据的质量取决于分布或特定任务（例如推理或预测）的一般相似性。显然，立即满足所有目标是一个巨大的挑战。我们邀请学术界共同推进隐私品质的边界。
Recent advances in generating synthetic data that allow to add principled ways of protecting privacy -- such as Differential Privacy -- are a crucial step in sharing statistical information in a privacy preserving way. But while the focus has been on privacy guarantees, the resulting private synthetic data is only useful if it still carries statistical information from the original data. To further optimise the inherent trade-off between data privacy and data quality, it is necessary to think closely about the latter. What is it that data analysts want? Acknowledging that data quality is a subjective concept, we develop a framework to evaluate the quality of differentially private synthetic data from an applied researcher's perspective. Data quality can be measured along two dimensions. First, quality of synthetic data can be evaluated against training data or against an underlying population. Second, the quality of synthetic data depends on general similarity of distributions or specific tasks such as inference or prediction. It is clear that accommodating all goals at once is a formidable challenge. We invite the academic community to jointly advance the privacy-quality frontier.
